neuron

Send WhatsApp Message

neuron_send_whatsapp

Send a WhatsApp message to a phone number or group. Auto-resolves which channel to use (org default > first connected). Supports text, image, audio, video, and document types.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	Yes	Recipient: phone number (e.g., '2348012345678') or group JID (e.g., '120363XXX@g.us')
`text`	Yes	Message text content
`sendAt`	No	ISO 8601 date-time for scheduled delivery (e.g., '2025-12-31T10:00:00Z'). Message sends immediately if omitted.
`mediaUrl`	No	URL of media to attach (required for non-text message types)
`channelId`	No	Unique identifier (UUID) of a specific channel to override auto-resolution
`contactName`	No	Display name for the recipient contact
`messageType`	No	Message type: 'text' (default), 'image', 'audio', 'video', or 'document'

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations beyond falses, the description carries the burden. It states the tool sends messages and auto-resolves channels, and lists supported types. It lacks details on side effects (e.g., costs, rate limits, queuing) or what happens on failure. This is adequate but could be more transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each adding essential information: action, auto-resolution, supported types. No filler. The key verb and resource are front-loaded. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 7 parameters with full schema coverage and no output schema, the description covers the main purpose, channel resolution, and supported media types. It does not describe return values (implicitly the message send result) or scheduling behavior (present in schema), but is still fairly complete for a sending tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining channel auto-resolution (meaning of channelId override) and the range of message types supported. This helps an agent understand parameter intent beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool sends WhatsApp messages to phone numbers or groups, specifies auto-resolution of the channel, and lists supported media types (text, image, audio, video, document). This distinguishes it from sibling tools like neuron_send_message (which may be generic) and other WhatsApp-specific sending tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the channel auto-resolution behavior (org default > first connected), giving context on when channelId override is needed. However, it does not explicitly state when to use this tool over alternatives like neuron_send_message or neuron_compose_message, nor does it provide exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0

Disambiguation3/5

Most tools are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.

Naming Consistency4/5

The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.

Tool Count1/5

309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.

Completeness4/5

The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.

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